AI-Enabled Financial Marketing: Leveraging ML, Predictive Analytics, and Data-Driven Strategies for Customer Engagement

Abstract

This paper analyzes and compares machine learning models for classification, prediction, and segmentation of customer in real world business dataset for evaluation. To evaluate the model performance, metrics like accuracy, precision, recall, F1_score, and AUC are used, and clustering analysis defines the key behavioral patterns for the customers. Model strengths and customer characteristics are said to be visualized in an intuitive way through radar charts, 3D scatters plots, and treemaps. In terms of classification and predictive tasks, Random Forest and Decision Tree models are always better than their alternatives. The insights from the segmentation shows the kind of customer engagement and value they generate. The process emphasizes the importance of data driven decision making and evaluation of model.